Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Lee, Jae-Sung; Kim, Dae‐Won

doi:10.3390/e18110405

Cited by 15 publications

(7 citation statements)

References 48 publications

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“…Equation 10indicates that the score value of each feature is influenced by the entropy value of each label, and this fact implies Proposition 1 as follows [40].…”

Section: Label Subset Selectionmentioning

confidence: 98%

“…However, these MI-based score functions commonly require the calculation of the dependencies between all variable pairs composed of a feature and a label [14]. Thus, they share the same drawback in terms of computational efficiency because labels known to have no influence on the evaluation of feature importance are included in the calculations [15,40]. In contrast to our previous study, our method proposed in this study discards unimportant labels explicitly prior to any multilabel learning process.…”

Section: Multilabel Feature Selectionmentioning

confidence: 99%

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Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

Lee

Kim

2018

Complexity

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The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier.

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“…Equation 10indicates that the score value of each feature is influenced by the entropy value of each label, and this fact implies Proposition 1 as follows [40].…”

Section: Label Subset Selectionmentioning

confidence: 98%

Section: Multilabel Feature Selectionmentioning

confidence: 99%

Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

Lee

Kim

2018

Complexity

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“…Traditional multi-label feature selection methods can be divided into two groups: problem transformation and algorithm adaptation [32], [33]. The problem transformation methods involve two steps: (1) transforming the multilabel data sets to multiple groups of single-label data sets;…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Feature Selection Based on Min-Relevance Label

Pan

2023

IEEE Access

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Multi-label feature selection has been widely adopted to address multi-label data with highdimension features. It is critical to calculate label correlations for multi-label feature selection. Existing methods adopt different schemes to calculate label correlations, which obtain different importance of labels. However, there exist two issues regarding these schemes calculating label importance: first, previous schemes cannot predict the whole labels well because they only focus on the most important labels; second, most of important labels have similar classification information corresponding to redundant features. To this end, we use the mutual information metric to obtain different cores regarding label set rather than calculating the importance of labels. Afterwards, we capture features with respect to each core label, finally, obtaining an optimal feature subset. To verify the effectiveness, our method is compared to state-of-the-art multi-label methods on 16 real-world data sets with several evaluating metrics. The results of experiment proves that the proposed method has achieves the best classification performance among all multi-label feature selection methods.

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“…Conventional multi-label feature selection methods can be divided into two groups to deal with the multi-label data sets: problem transformation and algorithm adaptation [ 37 , 38 ]. The problem transformation methods include two steps: (1) transform the multi-label data set to numerous single-label data sets; (2) select the relevant features from the transformed data sets.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Zhang

Gao

et al. 2020

Entropy

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Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.

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Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Cited by 15 publications

References 48 publications

Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

Multi-Label Feature Selection Based on Min-Relevance Label

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

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